The Need for MORE: Unsupervised Side-Channel Analysis with Single Network Training and Multi-output Regression

Ioana Savu, Marina Krcek, Guilherme Perin, Lichao Wu, Stjepan Picek

Abstract: This work explores the performance of multi-output regression models in side-channel analysis. We start with the recently proposed multi-output regression (MOR) approach for non-profiling side-channel analysis. Then, we significantly improve its performance by updating the loss function and distinguisher, then employing a novel concept of validation set to reduce overfitting. We denote our approach as MORE - Multi-Output Regression Enhanced, which emphasizes significantly better attack performance than MOR. Our results demonstrate that combining the MORE methodology, ensembles, and data augmentation presents a potent strategy for enhancing non-profiling side-channel attack performance and improving the reliability of distinguishing key candidates.